Systems and methods for providing predictive distance-to-empty for vehicles

ABSTRACT

Systems and methods are disclosed for providing predictive distance-to-empty (DTE) assessments for vehicles that can include electric, gas, or hybrid vehicles. An example method includes determining a plurality of learned parameters of vehicle operation of a vehicle based on a plurality of energy consumption parameters of the vehicle; determining a plurality of predictive parameters of vehicle operation of the vehicle selected from any combination of weather data or navigation data, the navigation data being determined relative to a planned route and applying a DTE function for the energy source, the DTE function utilizing the plurality of learned parameters of vehicle operation, and the plurality of predictive parameters of vehicle operation of the vehicle, based on a current capacity of the energy source to determine a DTE for the vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to, the benefit of, and is adivisional application of 16/353,922, filed Mar. 14, 2019, which ishereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to systems and methods that providepredictive distance-to-empty (DTE) assessments for vehicles that caninclude electric, gas, or hybrid vehicles. Some embodiments allow forthe use of real-time feedback and modeling of vehicle parameters basedon machine learning.

BACKGROUND

Distance-to-empty (DTE) is a distance number indicating the distancethat a vehicle can be driven before the battery, or energy source, isdepleted (for battery electric vehicles) or a fuel tank goes empty (forinternal combustion engine vehicles). It can be displayed to the driveron the vehicle instrument cluster or mobile apps. For battery electricvehicles, an accurate DTE is crucial to reduce a customer's rangeanxieties and increase the customer's comfort and confidence in drivingthe vehicle. To be sure, when the DTE is not accurate, customers may beat a disadvantage. In use cases such as battery electric vehicles(BEVs), users may be discouraged from considering a future electricvehicle purchase. These drawbacks may also negatively affect otherpotential customers' views on the vehicles, especially battery electricvehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingdrawings. The use of the same reference numerals may indicate similar oridentical items. Various embodiments may utilize elements and/orcomponents other than those illustrated in the drawings, and someelements and/or components may not be present in various embodiments.Elements and/or components in the figures are not necessarily drawn toscale. Throughout this disclosure, depending on the context, singularand plural terminology may be used interchangeably.

FIG. 1 depicts an illustrative architecture in which techniques andstructures for providing the systems and methods disclosed herein may beimplemented.

FIG. 2 is an example signal flow or schematic flow of adistance-to-empty (DTE) estimation process that can be performed inaccordance with various embodiments disclosed herein.

FIG. 3A is an example schematic diagram of a process for calculating anestimated DTE in a trip period.

FIG. 3B is an example schematic diagram of a process for calculating anestimated DTE in a post-trip period.

FIG. 4 is a flowchart of an example method that can be performed inaccordance with various embodiments disclosed herein.

DETAILED DESCRIPTION

Overview

In some embodiments, the systems and methods disclosed herein provideDTE analyses for vehicles, as well as actionable feedback to usersthrough a human machine interface of the vehicle. Generally, any vehiclecan utilize the predictive DTE systems and methods disclosed herein, butsome use cases enable a reduction in user anxiety and improve adoptionof certain vehicle types, such as battery electric vehicles. The systemsand methods herein may use vehicle historical energy consumption rateinformation (in the unit of kWh/km). The actual DTE is stronglydependent on future driving and environmental information, includingfuture temperature, future speed distribution, and future trip elevationchanges—just to name a few. Thus, the systems and methods herein providevarious means for obtaining and using predictive or future data toimprove DTE calculations.

Broadly, the systems and methods herein are configured, in someembodiments, to use vehicle connectivity to obtain predictiveinformation from a cloud system (service provider) or other third-partyresources, and utilize this information to provide an optimizedestimation of DTE for battery electric vehicles (BEVs). In someembodiments, this predictive DTE can be utilized with or without triproute information from a navigation device of the vehicle or anavigation service provider.

In various embodiments, available data for DTE estimations conducted inaccordance with the present disclosure include, but is not limited to,historical information learned from the vehicle's operation, informationabout a navigation map and elevation obtained either onboard or from aservice provider (e.g., a cloud system), and information obtained fromthe cloud system about traffic and/or weather forecasts. The informationfrom the service provider or third-party system is informative sincetraffic congestion and future temperature have an impact on DTEestimations. Bringing traffic and weather forecast data into the DTEanalyses disclosed herein significantly improves the accuracy of the DTEestimation.

In general, vehicles adapted for use in accordance with the presentdisclosure include a vehicle controller that can be configured toperform aspects of machine learning. For example, in a learning phase,the vehicle controller can learn parameters that can describe a driver'sdriving style, climate control preferences, vehicle accessory usage, andaverage weekly vehicle operation schedule. When the driver provides atrip plan through the navigation functionality of the vehicle, thevehicle controller can communicate with the service provider to obtaintraffic speed and outdoor temperature for a planned trip or route. Thenthe vehicle controller can combine these data with elevation data(obtained from either an onboard map or a service provider map) andlearned parameters (on driving style and climate control preferences) toestimate the energy consumption for the planned route. The vehiclecontroller may also obtain weather forecast data from the serviceprovider, and combine the weather forecast data with learned parameters(on driving style, climate control preferences, and weekly schedule asan example) to estimate an energy usage rate for a future time framethat extends after the planned trip is completed.

Finally the vehicle controller can also combine the estimated energyusage information and provide a DTE estimation in an ad-hoc manner, withor without regard to any planned route. In some embodiments, aspects ofroute planning can include calculations of a trip period (horizon withina trip) of a planned route, as well as a post-trip period (horizonbeyond the trip) of the planned route.

That is, the DTE estimation systems and methods herein can be executedwith or without a planned route. If the driver does not use a navigationsystem, the systems and methods herein can improve DTE accuracy byincorporating weather forecast data from the service provider andconsider DTE changes due to climate control energy usage. These andother aspects and advantages of the present disclosure are described ingreater detail herein with reference to the collective drawings.

Illustrative Architecture

Turning now to the drawings, FIG. 1 depicts an illustrative architecture100 in which techniques and structures of the present disclosure may beimplemented. The illustrative architecture 100 may include a serviceprovider 102, a vehicle 104, and a network 106. The network 106 mayinclude any one or a combination of multiple different types ofnetworks, such as cable networks, the Internet, wireless networks, andother private and/or public networks. In some instances, the network 106may include cellular, Wi-Fi, or Wi-Fi direct. In some embodiments, thearchitecture 100 can comprise one or more third-party systems such asthird-party system 107 that provides external data related to aspectssuch as weather data or traffic data. The service provider 102 can beimplemented in a cloud computing environment or as a virtual or physicalserver. In some embodiments, the service provider 102 comprises at leasta processor 101 and memory 103. The processor 101 is configured toexecute instructions stored in memory 103 to provide one or more of theDTE estimation features disclosed herein, either alone or in conjunctionwith the vehicle 104 (specifically the vehicle controller 108 disclosedbelow).

Generally, the vehicle 104 comprises a vehicle controller 108 thatcomprises a processor 110 and a memory 112. The memory 112 comprisesmodules such as an onboard machine learning module 114, a dataaggregation module 116, and a feedback comparator module 118. Thevehicle 104 generally comprises a drivetrain system 105, a climatecontrol system 107, one or more vehicle accessories 109, and an energysource 111. Also, the service provider 102 includes an efficiencyprediction module 120 that performs DTE calculations or estimates. Eachof these elements or modules is discussed in terms of individual andcollective operations with respect to FIG. 2 .

In some embodiments, the features and functions of the presentdisclosure related to DTE estimation can be performed entirely by theservice provider 102. In other embodiments, the DTE estimations can beperformed at the vehicle level by the vehicle controller 108, and inother embodiments the DTE estimation can be executed cooperativelybetween the service provider 102 and the vehicle controller 108 as willbe described in greater detail herein.

FIGS. 1 and 2 collectively illustrate an example application signal flowwhere the vehicle controller 108 of the vehicle 104 provides varioustypes of output that can be used by components of the service provider102. In some embodiments, the vehicle controller 108 continuously orperiodically collects and updates signals 124 related to a plurality oflearned parameters of vehicle operation of the vehicle. In someembodiments, this collection task runs continuously onboard and updateslearned parameters. These learned parameters are indicative of how adriver of the vehicle 104 uses energy in different scenarios, and whenthe vehicle 104 is usually operated on each day of a week.

In various embodiments, nominal values on parameters can be used beforeany learning starts. As the vehicle 104 operates, the onboard machinelearning module 114 of the vehicle controller 108 can be executed, andthe parameters can be updated to represent a behavior of the vehicle.The plurality of learned parameters can be stored onboard in the memory112 of the vehicle controller 108. In some embodiments, the plurality oflearned parameters can also be uploaded to the service provider 102 forstorage. In some embodiments, the plurality of learned parameters can bebatched for asynchronous transmission from the vehicle 104 to theservice provider 102 as desired.

The efficiency prediction module 120 of the service provider 102 can beconfigured to utilize the signals 126 as DTE input, as well as thelearned parameters, the current vehicle status, and traffic and weatherinformation obtained from the third-party system (collectively externalcloud API 122, which is used to access the third-party system(s) 107).The efficiency prediction module 120 can be executed when a DTEcalculation request is requested. Again, the efficiency predictionmodule 120 receives DTE values from input signals 126 from individualvehicle modules or in aggregate from the vehicle controller 108. Theefficiency prediction module 120 calculates DTE estimate values 123 andcan also output DTE estimate values for display on a human machineinterface (HMI) 128 of the vehicle 104.

According to some embodiments, the feedback comparator module 118 of thevehicle controller 108 can be executed to compare estimated energyconsumption with actual energy consumption for a trip with a knownroute. The feedback comparator module 118 can receive signals 130 forfeedback comparison from the vehicle controller 108. The result may bedisplayed on the HMI 128 in some embodiments. In some embodiments, thefeedback comparator module 118 can monitor whether an actual route takenby the vehicle 104 is the same or substantially similar to the plannedroute. In some embodiments, a comparison is not made between theestimated energy consumption and the actual energy consumption when theactual route deviates from the planned route. Additional details on theoperations of the feedback comparator module 118 are provided infra.

According to some embodiments, the data aggregation module 116 of thevehicle controller 108 can be configured to collect and aggregatevarious vehicle data, such as signals 132 that are not covered by otherdata collection channels provided to the service provider 102 by thevehicle controller 108. Because the size of high-frequency data may betoo large to send to the service provider 102 directly, some level ofonboard aggregation can be implemented. The data aggregation module 116of the vehicle controller 108 can execute continuously (or periodically)onboard the vehicle, allowing the vehicle controller 108 to transmitthese data to the service provider 102 at a lower frequency. Forexample, the vehicle controller 108 can transmit aggregated data afterevery trip, or at the beginning or end of every day. The uploaded datamay be stored for further analysis by the service provider 102.Additional details regarding collection and assessment of a plurality oflearned parameters of vehicle operation will be provided in greaterdetail herein.

According to some embodiments, energy consumption and energy consumptionrate values can be calculated over two distinct portions or periods of aplanned route. In some embodiments, the DTE value is calculated using anaggregate or combination of two energy estimates. As noted above, theseperiods of time can include a trip period and/or a post-trip period. Forthe trip period, a total energy consumption (kWh) can be estimated bythe efficiency prediction module 120 of the service provider 102.Weather data and traffic speed data can be used in the energy estimationby the efficiency prediction module 120. For the post-trip period (e.g.,from the end of the trip, to the depletion of the energy source 111), anoverall energy rate (kWh/km) can be estimated for the post-trip period.Weather forecast data can be used in the post-trip energy consumptionestimation as well. In general, available battery energy is determinedas well (also referred to as a current capacity of an energy source 111of the vehicle 104). This value can be received from the vehiclecontroller 108.

In general, an example method or algorithm for estimating DTE over bothof these periods includes determining an available energy level of theenergy source 111 and subtracting a predicted energy usage (e.g.,efficiency) for a trip period. This calculation provides a remainingenergy level of the energy source 111 for any post-trip period, whichcan be utilized to determine a remaining DTE value for the energy source111. When a post-trip distance is known, this can be utilized incombination with the remaining energy level of the energy source 111 anda predicted energy usage (e.g., efficiency) for the post-trip period tocalculate a DTE over the two periods.

In more detail, if a distance of a trip with a known route is D_(trip),the battery energy available before a trip (current capacity of theenergy source 111 of the vehicle 104) is E_(bat), the predicted totalenergy consumption for the trip is E_(trip), the predicted overallenergy rate beyond the trip (e.g., post-trip period) is η_(beyond), thenthe DTE value will be calculated as follows:

$D_{DTE} = {D_{trip} + {\frac{E_{bat} - E_{trip}}{\eta_{beyond}}.}}$

A predicted overall energy rate beyond the trip η_(beyond) is calculatedby determining a distance of the post-trip operation of the vehicleusing a suitable method (examples disclosed infra). Then, the methodincludes estimating the energy consumption over the post-trip operationof the vehicle (e.g., post-trip period) and then estimating the energyrate over the post-trip period of the vehicle.

This estimated energy rate can be used as the energy rate from the endof the trip, to the depletion of the energy source 111 of the vehicle104, to calculate the DTE value. It will be understood that thepost-trip operation estimates may be slightly longer or shorter than theoverall DTE estimate, because it can be calculated before the DTEestimate is obtained. This is acceptable since it will be assumed thatthe average energy rate during the post-trip operation of the vehicleand the DTE range are very close to one another in value.

The following paragraphs provide descriptions of energy consumptionmodels for use in accordance with the present disclosure. In someembodiments, the energy consumed by the vehicle 104 can be divided intofour categories (e.g., plurality of energy consumption parameters): (1)energy used for driving the wheels through the drivetrain system 105,(2) energy used for climate control through the climate control system107, (3) energy used for accessories through the one or more vehicleaccessories 109, and (4) energy loss because of external factors (e.g.,low temperature).

In some embodiments, energy consumption for the drivetrain system 105can be learned in secondary states such as when a trailer is being towedby the vehicle 104. Learning may take place by the vehicle controller108 for additional states specific to individual trailers so thatadjustments can be made when a specific trailer is connected. Thetypical mass loading, vehicle dynamics, and aerodynamic propertiesimposed by the trailer on the vehicle 104 can be reflected across thefour parameters listed above by tracking energy consumption separatelyfor these secondary states. Thus, the vehicle controller 108 can beconfigured to determine when a triggering condition occurs, such as whena trailer is connected to the vehicle 104. This could include sensingwhen a trailer is plugged into a trailer interface of the vehicle 104.

For both portions of a planned route, such as the trip period and thepost-trip period, the energy consumption for the four energy consumptionparameters is modeled by the onboard machine learning module 114 of thevehicle controller 108 and can be calculated when a DTE calculationrequest is received. The learned driving energy consumption rates indifferent scenarios, plus the predictive information including theweather forecast, traffic conditions, and elevation information, can beused in the energy consumption models to provide an accurate estimationof DTE.

FIG. 3A is a schematic diagram that illustrates energy consumption datacalculations performed for a trip period of a planned route. Thisprocess is referred to as determining a plurality of energy consumptionparameters for a vehicle on a planned route. The plurality of energyconsumption parameters can be referred to as “first” parameters whencalculated in reference to the trip period. More specifically, theprocess illustrated in FIG. 3A includes calculating energy consumptiondata for a trip period of a planned route. It will be understood thatsome processes included in the energy consumption data calculations arederived from learned processes as mentioned above. Some data is obtainedfrom third-party resources, such as weather and/or traffic data.

In some embodiments, the process includes calculating a driving-basedenergy consumption parameter 302 based on any combination of a distancethat the vehicle will travel on a trip period (route and map data withelevation 304) over a specified road class 306, along with real-timetraffic data 308, and a learned road class efficiency 310 for thevehicle. In some embodiments, the process comprises calculating atemperature-based energy loss parameter 312 based on any combination ofa learned temperature-based energy loss 314 for the vehicle based onoutdoor temperature data 316, and a learned cold start-based energy loss318 for the vehicle which is a function of the outdoor temperature data316 and a current vehicle temperature 320 (e.g., initial vehicletemperature).

In various embodiments, the process for calculating the energy usage fora trip period further comprises calculating a first climatecontrol-based energy consumption parameter 322 based on any combinationof a learned transient extra energy for climate control 324, which is afunction of an initial cabin temperature data 326 and the outdoortemperature data 316, and a learned efficiency for climate control 330which is a function of the outdoor temperature data 316. In someembodiments, the process for calculating the energy usage for a tripfurther comprises calculating a first vehicle accessories-based energyconsumption parameter 332 based on a learned efficiency for vehicleaccessories 334. In various embodiments, the energy consumptionparameters are calculated with reference to a trip time 335 (e.g., thelength of vehicle operation when in a trip period).

FIG. 3B is a schematic diagram that illustrates energy consumption datacalculations performed in a post-trip or beyond the horizon time frame.The plurality of energy consumption parameters can be referred to as“second” parameters when calculated in reference to the post-tripperiod. In some embodiments, the method includes calculating energyconsumption for operating the vehicle after completion of the plannedroute by calculating a driving-based energy consumption parameter 336based on an expected energy consumption 338 for driving the vehicle. Invarious embodiments, the method can include calculating atemperature-based energy loss parameter 340 based on any combination ofa learned temperature-based energy loss 342 for the vehicle based onfuture outdoor temperature data 344, and a learned cold start-basedenergy loss 346 for the vehicle which is a function of the futureoutdoor temperature data 344 and a predicted initial vehicle temperature348. It will be understood that the future outdoor temperature data 344can be obtained from any third-party platform or service providingweather data.

In various embodiments, the method can include calculating a climatecontrol-based energy consumption parameter 350 based on a learned extratransient energy for climate control 352, which is a function of apredicted initial cabin temperature data 354 and the future outdoortemperature data 344. This calculation can also include a learnedefficiency for climate control 356 which is a function of the futureoutdoor temperature data 344. In some embodiments, the climatecontrol-based energy consumption parameter 350 is further affected by anexpected or predicted number of cold starts 358 that may occur. The coldstart predictive measures can also be used in the calculation of thetemperature-based energy loss parameter 340. According to someembodiments, the method can also comprise utilizing the first vehicleaccessories-based energy consumption parameter 360 that is a function ofa learned efficiency of vehicle accessories 362. In some embodiments,these values are determined as a function of an expected length of atrip (trip time 364).

Additional descriptive details regarding the calculation of energyconsumption using the aforementioned plurality of energy consumptionparameters are provided in greater detail in the following paragraphs.Again, these energy consumption analyses are discussed first withreference to a trip period and then with respect to a post-trip period.

In general, the energy consumption within the trip period is a sum ofthe energy consumption across the four aforementioned energy consumptionparameters according to the following equation:E _(trip) =E _(dr) +E _(cl) +E _(acc) +E _(lo)

It will be understood that E_(dr) represents the driving-based energyconsumption parameter, which relates to the drivetrain system 105 of thevehicle 104 (see FIG. 1 ). E_(cl) represents the climate control-basedenergy consumption parameter which relates to the climate control system107 of the vehicle 104. In some embodiments, E_(acc) represents thevehicle accessories-based energy consumption parameter which relates tothe vehicle accessories 109 of the vehicle 104, and E_(lo) representsthe temperature-based energy loss parameter and how temperature inducesloss in the energy storage 111 of the vehicle 104.

The driving-based energy consumption parameter E_(dr) within a tripperiod can be calculated using the following equationE_(dr)=Σ_(i)η_(i)·D_(i), where η_(i) is the learned energy rate (kWh/kmor kilowatts per hour, per kilometer) on Road Class i, and D_(i) (km) isthe distance on Road Class i for the trip period. The road class isdefined by an average speed on the road and a road grade (determinedfrom navigation or map data).

The value of η_(i) can be obtained directly from the saved learnedparameters in some instances. The value of D_(i) can be obtained fromprocessed route data with real-time traffic speed and elevationinformation received from a third-party traffic or navigation dataservice.

The climate control-based energy consumption parameter E_(cl) can becalculated using the following equation: E_(cl)=μ_(cl)·t+k_(cl), whereμ_(cl) is a learned energy rate (kWh/s or kilowatt per hour, per second)for maintaining the climate setting of the climate control system 107 ofthe vehicle 104 under a current temperature condition (e.g., steadystate energy usage rate), t (s) is the trip period time, and K_(cl)(kWh) which is a learned extra energy usage for bringing the currentcabin temperature up or down to the target temperature (e.g., transientenergy usage).

The value of μ_(cl) can be obtained from a lookup table of learnedparameters. The input to the lookup table includes the outsidetemperature T_(out), which is obtained from a third-party weatherservice or system (see third-party system 107 of FIG. 1 ), in someembodiments. Thus, μ_(cl) is determined based on a relationship ofμ_(cl)=μ_(cl)(T_(out)) in some instances. An example lookup table isprovided below:

TABLE 1 Outside T. <−40 −30 −20 . . . 30 40 >50 Efficiency

If the trip period time frame is long and the temperature change issignificant along the trip due to time and location change, the averageefficiency from multiple temperature values can be used and calculatedusing

${\mu_{cl} = \frac{{\mu_{cl}\left( T_{{out},1} \right)} + {\mu_{cl}\left( T_{{out},2} \right)} + \ldots + {\mu_{cl}\left( T_{{out},n} \right)}}{n}},$where the value of the trip period time t is estimated by the navigationsystem or vehicle controller of the vehicle. The value of K_(cl) can beobtained from another example lookup table of learned parameters. Theinputs to the lookup table are the initial cabin temperature T_(cab) andthe outside temperature T_(out). Thus, K_(cl)=K_(cl)(T_(cab), T_(out)).In some embodiments, the cabin temperature is obtained from the vehicle,and the outside temperature is obtained from a third-party weather dataservice or system.

TABLE 2 Initial Cabin T.\Outside T. <−40 −30 −20 . . . 30 40 >50 <−40 .. . >50

The vehicle accessories-based energy consumption parameter can becalculated using E_(acc)=μ_(acc)·t, where μ_(acc) is the learned energyrate (kWh/s) for accessory energy, and t is the trip period time. Thevalue of μ_(acc) can be obtained directly from learned parameters.

The temperature-based energy loss parameter (e.g., energy loss as aresult of external factors) can be calculated usingE_(lo)=μ_(lo)·t+K_(lo), where μ_(lo) the learned energy rate (kWh/s) formaintaining the vehicle temperature under the current temperaturecondition (steady state energy usage rate), t is the trip period time,and K_(lo) is the learned extra energy usage for bringing the currentvehicle temperature up to the target temperature (transient energyusage). The value of μ_(lo) is obtained from the lookup Table 1 whichincludes learned parameters. The input to the lookup table is theoutside temperature T_(out). Again, μ_(lo) is determined according to arelationship μ_(lo)=μ_(lo)(T_(out)), in some embodiments. If the trip islong and the temperature change is significant along the trip due totime and location change, the average efficiency from multipletemperature values can be used. The following equation can be used tocalculate the average value

${\mu_{lo} = \frac{{\mu_{lo}\left( T_{{out},1} \right)} + {\mu_{lo}\left( T_{{out},2} \right)} + \ldots + {\mu_{lo}\left( T_{{out},n} \right)}}{n}},$where the value of K_(lo) is obtained from a lookup Table 2 of thelearned parameters. The inputs to the lookup table are the initial cabintemperature T_(cab) and the outside temperature T_(out). Again,K_(lo)=K_(lo)(T_(cab), T_(out)), in some embodiments.

The following description provides details regarding calculating energyconsumption for operating the vehicle after completion of the plannedroute. This period was referred to as a post-trip period orbeyond-the-trip prediction horizon. This period starts at the end of thetrip period, which has a known route, and ends at a roughly estimatedDTE end. The roughly estimated DTE uses the following DTE algorithm:D_(beyond,start)=D_(trip), and

${D_{{beyond},{end}} = {D_{trip} + \frac{E_{bat} - E_{trip}}{\eta_{ave}}}},$where η_(ave) is the overall average energy rate (kWh/km). In someembodiments, this is a pre-calibrated constant. Thus,D_(beyond)=D_(beyond,end)−D_(beyond,start), in some embodiments.

It should be noted that this distance may be adjusted as D_(beyond,end)may be adjusted. For example, if the time horizon of this distance islonger than 10 days, it may be truncated to an estimated 10-day drivingdistance using historical weekly operation data, if the weather forecastis only available for 10 days. Again, the post-trip period orbeyond-the-trip prediction horizon start time is the end time of thetrip period using the known route. Its end time is determined using thelearned weekly operation pattern table (additional details on learnedparameters are described infra). An example of the learned weeklyoperation pattern table is shown below in Table 3.

TABLE 3 Mon. Mon. Mon. Tue. Tue. Fri. 0-2 am 2-4 am 4-6 am . . . 6-8 am8-10 am . . . 4-6 pm . . . Average Distance 0.5 0 0 . . . 10 1 . . . 15. . . (km) Average Trip Time 30 0 0 . . . 1600 60 . . . 1700 . . . (s)Average Energy for 0.2 0 0 . . . 2 0.2 . . . 2.5 . . . Driving (kWh)Average Number of 0.1 0 0 . . . 0.9 0 . . . 0.8 . . . Cold Start AverageTemperature −10 0 0 . . . 0.9 0 . . . 0.8 . . . Difference of Cold Start(T_cab − T_out)

In some embodiments, an average driving distance in every time window ina week can be stored in Table 3 as the learned parameters. Then,starting from a beginning of the post-trip period time, after coveringthe prediction horizon distance, the end time can be found using Table4.

TABLE 4 Mon. Mon. Mon. Tue. Tue. Fri. . . . 0-2 am 2-4 am 4-6 am . . .6-8 am 8-10 am . . . 4-6 pm . . . Average 0.5 0 0 . . . 10 1 . . . 15 .. . Distance (km) Beyond the trip prediction horizon-e.g. 120 km

In some embodiments, if the horizon passes the end of a week, it shouldbe continued to the next week. The operation pattern for the next weekis considered the same as the stored table, in various embodiments. Inother embodiments, if the horizon end is longer than 10 days after thecurrent day, it may be cut off at the 10^(th) day, and the post-tripperiod or beyond-the-trip prediction horizon distance D_(beyond) may beadjusted accordingly as well. The new distance is understood to includea sum of the average driving distances from the start of the horizon tothe end of the 10^(th) day. While 10-day examples have been providedherein, other time frames can likewise be utilized.

The method can also include decomposing the energy consumption in thepost-trip period. The energy consumption in the post-trip period may becalculated as a sum of the energy consumption in every time windowwithin the post-trip period as follows: E_(beyond)=Σ_(j)E_(beyond,j).The time windows may be defined the same as the time windows in theweekly operation pattern learning. Relating back to Table 4, the firsttime window (Mon. 0-2 am) and the last time window (Fri. 4-6 pm), arevery likely to be only partially covered by the prediction horizon. Inthis example, various compensating processes can be used. In oneprocess, the system can consider that it is a fully included timewindow, and adjust the post-trip period prediction horizon distanceaccordingly. In other embodiments, the system can discard one or moretime windows, and adjust the post-trip period prediction horizondistance accordingly. In yet another embodiment, one or more of thewindows can be considered as a partially included time window, and theestimated energy consumption can be adjusted by a ratio.

In various embodiments, a calculation of energy consumption in each ofthe time windows can be performed. Within each time window within thepost-trip period prediction horizon, the sum of the energy consumptionin the four parameters can be calculating using:E_(beyond,j)=E_(dr)+E_(cl)+E_(acc)+E_(lo). Again, E_(dr) represents thedriving-based energy consumption parameter, E_(cl) represents theclimate control-based energy consumption parameter, E_(acc) representsthe vehicle accessories-based energy consumption parameter, and E_(lo)represents the temperature-based energy loss parameter.

With respect to the driving-based energy consumption parameter, theenergy for driving the wheels in a time window may be the historicalaverage energy for driving the wheels in this time window. It can bedirectly obtained from the learned weekly operation pattern table.

With respect to the climate control-based energy consumption parameter,the following equation can be used: E_(cl)=μ_(cl)·t+N·K_(cl), whereμ_(cl) is the learned energy rate (kWh/s), t (s) is the expected drivingtime, and K_(cl) (kWh) is the learned transient extra energy usage. N isthe expected number of trips with cold starts in this time window. Thevalue of μ_(cl) is obtained from a lookup table of the learnedparameters, as with the examples provided above (see Table 1). The inputto the lookup table is the outside temperature T_(out) at the time ofthe time window, which is obtained from a third-party weather dataservice or system. To be sure, μ_(cl)=μ_(cl)(T_(out)). In someembodiments, the value of t can be obtained from the learned weeklyoperation pattern table, the value of N can be obtained from the learnedweekly operation pattern table, and the value of K_(cl) can be obtainedfrom a lookup table of the learned parameters (see Table 2 as anexample). The input to the lookup table can include the initial cabintemperature T_(cab) and the outside temperature T_(out). In someembodiments, K_(cl)=K_(cl)(T_(cab), T_(out)), where the cabintemperature is obtained using T_(out) and the expected temperaturedifference ΔT is obtained from the learned weekly operation patterntable. In sum, T_(cab)=T_(out)+ΔT, in some embodiments.

In various embodiments, the vehicle accessories-based energy consumptionparameter can be calculated using E_(acc)=μ_(acc)·t, where μ_(acc) isthe learned energy rate (kWh/s) for accessory energy, and t is theexpected driving time. The value of μ_(acc) may be obtained directlyfrom the saved learned parameters.

According to some embodiments, the temperature-based energy lossparameter can be calculated using the following equation:E_(lo)=μ_(lo)·t+N·K_(lo), where μ_(lo) is the learned energy rate(kWh/s), t (s) is the expected driving time, and K_(lo) (kWh) is thelearned transient extra energy usage. N is the expected number of tripswith cold starts in this time window. The value of μ_(lo) is obtainedfrom a one-dimensional lookup table of the learned parameters. The inputto the lookup table is the outside temperature T_(out) at the time ofthe time window, which is obtained from a third-party weather dataservice or system. Thus, μ_(lo)=μ_(lo)(T_(out)), in some embodiments,where the value of t is obtained from the learned weekly operationpattern table. The value of N is obtained from the learned weeklyoperation pattern table, and the value of K_(lo) is obtained from atwo-dimensional lookup table of the learned parameters (see Table 2above). The inputs to the lookup table are the initial cabin temperatureT_(cab) and the outside temperature T_(out). In various embodiments,K_(lo)=K_(lo)(T_(cab), T_(out)), where the cabin temperature is obtainedusing T_(out) and the expected temperature difference ΔT is obtainedfrom the learned weekly operation pattern table. In sum,T_(cab)=T_(out)+ΔT, in various embodiments.

The energy rate for the post-trip period (e.g., beyond-the-trip horizon)can be calculated using the total energy consumption in the post-tripperiod prediction horizon divided by the distance of the post-tripperiod prediction horizon using the following equation:

$\eta_{beyond} = {\frac{E_{beyond}}{D_{beyond}}.}$

Referring back to FIG. 1 , as noted above, some data obtained by thevehicle controller of the vehicle may not be utilized in the numerouscalculations disclosed above, but nevertheless may provide value to avehicle operator, a vehicle manufacturer, or other similar entity. Also,as mentioned above, some embodiments include DTE calculations performedby the service provider 102 and specifically the efficiency predictionmodule 120 of the service provider 102. In some embodiments, the DTEestimates can be calculated onboard the vehicle 104 by the vehiclecontroller 108.

In various embodiments, energy rate results transmitted to the vehicle104 by the service provider 102 include not only the overall energyrate, but also additional information for the onboard modules tocalculate future DTE. This allows for DTE calculation or refinement evenwhen a connection to the service provider 102 is lost. In someembodiments, energy rate numbers (in kWh/km) and a trip length aretransmitted to the vehicle 104. If E_(dr) ^(t), E_(cl) ^(t), E_(acc)^(t), E_(lo) ^(t) are the energy consumption in four categories withinthe trip period, four energy rates

${\eta_{dr}^{t} = \frac{E_{dr}^{t}}{D_{trip}}},{\eta_{cl}^{t} = \frac{E_{cl}^{t}}{D_{trip}}},{\eta_{acc}^{t} = \frac{E_{acc}^{t}}{D_{trip}}},{{{and}\eta_{lo}^{t}} = \frac{E_{lo}^{t}}{D_{trip}}}$can be transmitted to the vehicle 104. If E_(dr) ^(b), E_(cl) ^(b),E_(acc) ^(b), E_(lo) ^(b) are the energy consumption in four categoriesin the post-trip period prediction horizon, four energy rates

${\eta_{dr}^{b} = \frac{E_{dr}^{b}}{D_{beyond}}},{\eta_{cl}^{b} = \frac{E_{cl}^{b}}{D_{beyond}}},{\eta_{acc}^{b} = \frac{E_{acc}^{b}}{D_{beyond}}},{{{and}\eta_{lo}^{b}} = \frac{E_{lo}^{b}}{D_{beyond}}}$may be sent. The trip distance D_(trip) may also be sent to the vehicle104.

According to some embodiments, comparisons between estimated energyconsumption and actual energy consumption can be performed in order toverify the accuracy of the DTE algorithms and improve or optimizecalculations. Before comparing the actual trip energy consumption withthe predicted energy consumption, it may be advantageous to compare theactual trip route with the planned trip route. The route comparison canbe done onboard using the vehicle controller 108. Therefore, if theplanned trip route geometry data is not available onboard, such data maybe sent to the vehicle 104 by the service provider 102. The plannedroute data is a sequence of longitudes and latitudes for importantgeometry waypoints along the route, and such data can be sent to thevehicle 104 before the trip starts.

Example methods for complete calculations of DTE estimates are providedbelow. Current data for battery energy available E_(bat) may be usedwhen calculating DTE estimates. A DTE estimate can be calculated using:

$D_{{CE} - {DTE}} = {{D_{trip} + \frac{E_{bat} - E_{trip}}{\eta_{beyond}}} = {D_{trip} + {\frac{E_{bat} - {D_{trip}\left( {\eta_{dr}^{t} + \eta_{cl}^{t} + \eta_{acc}^{t} + \eta_{lo}^{t}} \right)}}{\eta_{dr}^{b} + \eta_{cl}^{b} + \eta_{acc}^{b} + \eta_{lo}^{b}}.}}}$

During the trip period, if the distance into the trip is D, theremaining distance of the trip is D_(trip) ^(r)=D_(trip)−D, and

$D_{{CE} - {DTE}} = {D_{trip}^{r} + {\frac{E_{bat} - {D_{trip}^{r}\left( {\eta_{dr}^{t} + \eta_{cl}^{t} + \eta_{acc}^{t} + \eta_{lo}^{t}} \right)}}{\eta_{dr}^{b} + \eta_{cl}^{b} + \eta_{acc}^{b} + \eta_{lo}^{b}}.}}$After the trip, D_(trip) ^(r)=0, then

$D_{{CE} - {DTE}} = {\frac{E_{bat}}{\eta_{dr}^{b} + \eta_{cl}^{b} + \eta_{acc}^{b} + \eta_{lo}^{b}}.}$

The following sections provide additional details on aspects ofparameter learning that can be accomplished using the onboard machinelearning module 114 of the vehicle controller 108. While parameterlearning can be implemented onboard, output of the parameter learningcan be stored onboard, as well as uploaded to the service provider 102when desired. To be sure, these learned parameters are used in theexample processes of FIGS. 3A and 3B.

The following parameters can be learned by the onboard machine learningmodule 114. In some embodiments, energy rates (kWh/km) for driving thewheels on every road class can be determined. These are referred to aslearned road class efficiency for the vehicle. In some embodiments,these data can be stored as a one-dimensional table for η_(i), which isthe learned driving energy rate on Road Class i. In some embodiments,the onboard machine learning module 114 can learn steady state energyrates (kWh/s) for climate control under different outside temperatures.Again, these data can be stored in a one-dimensional lookup table forμ_(cl)(T_(out)). In various embodiments, the onboard machine learningmodule 114 can learn extra transient energy usage in climate control forbringing the current cabin temperature up or down to the targettemperature. As noted above, these data can be stored in atwo-dimensional lookup table for K_(cl)(T_(cab), T_(out)).

In some embodiments, the onboard machine learning module 114 can learnenergy rater (kWh/s) for accessories, which is a learned number μ_(acc).In one or more embodiments, the onboard machine learning module 114 canlearn steady state energy rate loss as a result of external factors(e.g., low temperature) (kWh/s) under different outside temperatures. Tobe sure, these data is stored in a one-dimensional lookup table forμ_(lo)(T_(out)). The onboard machine learning module 114 can learn extratransient energy usage loss as a result of external factors (e.g., lowtemperature), which can be stored in a two-dimensional lookup table forK_(lo)(T_(cab), T_(out)). In general, these various learned parameterscan be determined using the onboard machine learning module 114, whichmonitors signal output from each of the drivetrain system 105, theclimate control system 107, and the individual vehicle accessories 109.

In some embodiments, data collected from these various vehicle systemscan be stored. For example, vehicle weekly operation pattern parameterscan be obtained and stored in a table or other similar data structure.For example, a week can be divided into M time windows, and the averageoperation attributes in every time window can be learned and stored. Insome embodiments, the onboard machine learning module 114 can calculateand store an average driving distance in every time window, an averagedriving time in every time window, an average number of trip cold startsin every time window, an average energy for driving in every timewindow, and an average temperature difference T_(cab)−T_(out) at tripcold starts in every time window.

In some embodiments, when energy consumption parameters are learned,some initializing values may be used. That is, for parameter x to belearned, an initial value x₀ will be stored before learning by theonboard machine learning module 114 starts. These initial values shouldbe able to provide a reasonable DTE estimation before learning starts.If the parameter stored in the vehicle is x_(st), a new updated valuefor this parameter is x_(latest), and the new parameter stored can beupdated as a weighted sum of the old parameter stored and the latestvalue. This replacement process is represented as:x_(st+)=λ·x_(st)+(1−λ)·x_(latest), where λ can be considered as aforgetting factor. Each parameter can utilize a different forgettingfactor in some embodiments.

After initializing values are used, the calculations of various energyconsumption parameters can be updated using real-time acquired data fromthe various components of the vehicle that contribute to energyconsumption and/or loss. In some embodiments, energy rates (kWh/km) fordriving the wheels on every road class can be updated. In one example,for every one km driven by the vehicle, the average speed ν_(ave) of thevehicle can be calculated as follows:

$v_{ave} = {\frac{d_{end} - d_{start}}{t_{end} - t_{start}}.}$Also, an average road grade θ_(ave) is obtained (can be obtained fromnavigation data from a third-party navigation or mapping system or froman onboard navigation or mapping feature). The energy used for drivingthe wheels during this period is E_(dr). Thus, the energy rate iscalculated as:

$\eta_{dr} = {\frac{E_{dr}}{d_{end} - d_{start}}.}$If ν_(ave) and θ_(ave) fall into Road Class i, η_(dr) can be used toupdate η_(i) using the general parameter updating rules.

Steady state energy rates (kWh/s) for climate control under differentoutside temperatures can also be updated. During vehicle operation,after the first 10 minutes (example time frame that can be varied), andevery 10 minutes, the climate control energy usage is E_(cl), and theenergy rate for climate control is

$\mu_{cl} = {\frac{E_{cl}}{t}.}$An outside temperature is T_(amb). μ_(cl) and this value can be used toupdate the corresponding value in the one-dimensional lookup table forμ_(cl)(T_(out)) using the general parameter updating rules.

Extra transient energy usage in climate control for bringing the currentcabin temperature up or down to the target temperature can also beupdated. During vehicle operation, in the first 10 minutes (example timeframe that can be varied), the climate control energy usage is E_(cl).The extra transient energy usage in climate control isE_(cl_t)=E_(cl)−μ_(cl)(T_(out))·t. The outside temperature is T_(amb).The cabin temperature at trip start T_(cab). E_(cl_t) may be used toupdate the corresponding value in the two-dimensional lookup table forK_(cl)(T_(cab), T_(out)) using the general parameter updating rules. Ifthe trip is shorter than 10 minutes (example time frame that can bevaried), no update may be made to the K_(cl)(T_(cab), T_(out))two-dimensional lookup table.

In various embodiments, an energy rate (kWh/s) for accessories can beupdated. During vehicle operation, if the accessory energy is E_(acc),then the energy rate for accessories is

$\mu_{acc} = {\frac{E_{acc}}{t}.}$μ_(acc) and may be used to update the stored energy rate for theaccessories value using the general parameter updating rules.

In various embodiments, the steady state energy rate loss as a result ofexternal factors (e.g., low temperature) (kWh/s) under different outsidetemperatures can be updated. During vehicle operation, after the first10 minutes (again, merely an example), and every 10 minutes, the energyloss is E_(lo), and the energy rate for energy loss is

$\mu_{lo} = {\frac{E_{lo}}{t}.}$The outside temperature is T_(amb). μ_(cl) and may be used to update thecorresponding value in the one-dimensional lookup table forμ_(lo)(T_(out)) using the general parameter updating rules.

Extra transient energy usage loss as a result of external factors (e.g.,low temperature) can also be updated in some instances. During vehicleoperation, in the first 10 minutes, the energy loss is E_(lo). The extratransient energy loss in climate control isE_(lo_t)=E_(lo)−μ_(lo)(T_(out))·t. The outside temperature is T_(amb).The cabin temperature at trip start is T_(cab)·E_(lo_t) and may be usedto update the corresponding value in the two-dimensional lookup tablefor K_(lo)(T_(cab), T_(out)) using the general parameter updating rules.

Again, if the trip is shorter than 10 minutes (or shorter than whateverestablished time frame is used), no update may be made to theK_(lo)(T_(cab), T_(out)) two-dimensional lookup table.

In some embodiments, vehicle operation pattern parameters can be updatedon a daily or a weekly basis. In some embodiments, an update isimplemented at the end of each time window and/or at the start of everytrip. At the start of every trip, a check can be performed if there aretime windows that were not updated between the last update and thecurrent time. If there are time windows that were not updated, an updatefor those time windows can be performed. Even if the vehicle was notrunning during a time window, an update is needed because some (but notall) of the average attributes may change if the driving time was zero.Some updates are conducted using the general parameter updating rules.For example, an average driving distance in every time window can beupdated. The driving distance is the total distance in the time window,which may be the sum of the distances in multiple trips. If the vehicleis not driven in the time window, the updated value for the averagedriving distance is zero. Also, an average driving time in every timewindow can be updated. The driving time is the total time in the timewindow, which may be the sum of the driving time in multiple trips. Ifthe vehicle is not driven in the time window, the updated value for theaverage driving time is zero.

In various embodiments, the average number of trip cold starts in everytime window can be updated. To be sure, a cold start is defined as avehicle start event that is at least 60 minutes after the last vehicleshutdown. If the vehicle is not driven in the time window, the updatedvalue for the average number of cold starts is zero.

In some embodiments, an average energy for driving in every time windowcan be updated. The driving time is the total energy for driving thewheels in the time window, which may be the sum of multiple trips. Ifthe vehicle does not run in the time window, the updated value for theaverage energy for driving is zero. Also, an average temperaturedifference T_(cab)−T_(out) at trip cold starts in every time window canbe updated. This temperature difference is calculated at trip coldstarts. If there are multiple cold starts in a time window, multipleupdates are needed. If there are no cold starts in a time window, thisvalue may not be updated.

As noted throughout, the systems and methods disclosed herein canoperate in a real-time or continuous manner in some embodiments, wherelearning parameters are being updated in real-time or near-real-timefrom feedback such as comparisons of the estimated DTE and the actualDTE or the estimated energy consumption versus the empirical or actualenergy consumption. To be sure, discrepancies between estimated versusactual values for any of these measurements indicates a need to improvethe DTE or energy consumption methods.

According to some embodiments, a buffering of the predicted total energyfor the trip period having a known route is performed. In someembodiments, when a DTE estimate is calculated, the data sent from theservice provider 102 to the vehicle 104 includes information about thepredicted total trip energy usage E_(trip) ^(pred), where E_(trip)^(pred)=D_(trip)(η_(dr) ^(t)+η_(cl) ^(t)+η_(acc) ^(t)+η_(lo) ^(t)).E_(trip) ^(pred) may be stored in the vehicle controller 108 until adetour from a planned route is detected or the trip is completed and thetrip energy feedback comparison is made.

The vehicle controller 108 can be configured to detect a detour in atrip (e.g., planned trip). The comparison of the predicted energyconsumption and the actual energy consumption may only be made if thevehicle follows the route used to calculate the DTE. Two example typesof detour detection may be implemented by the vehicle controller 108 todetermine if the vehicle really follows the planned route. In oneembodiment, the vehicle controller 108 is configured to detect a detourusing GPS and route geometry. In some embodiments, this process involvesinterpolating route waypoints. The route geometry data sent from theservice provider 102 to the vehicle 104 can comprise a sequence ofwaypoints. The road segment between two points is considered a straightline. If the distance between two adjacent points is longer than 500meters, one or more interpolation points may be added. The resultantsequence of points should have a maximum distance of 500 meters betweentwo adjacent points. Next, the vehicle controller 108 can detect adetour by calculating the distance from the vehicle to the route.Detecting a detour by comparing the vehicle GPS and route geometry canbe conducted periodically on the vehicle (every 30 seconds or one minuteas an example).

The distance from the vehicle's current GPS location to the closestwaypoint in the route geometry sequence can be calculated. There are afew ways to make the calculation more efficient than looping through allpoints. In some embodiments, the vehicle controller 108 can build ak-dimensional tree of all route waypoints at the start of the trip, anduse the k-dimensional tree to find the closest point and the closestdistance. Alternatively, the vehicle controller 108 can track theclosest point, the route distance from start to the closest point, andthe distance driven for the trip. The vehicle controller 108 can usethis information to narrow down the closest point candidates and onlyloop through a few points to find the closest. If the distance from thevehicle to the closest waypoint on the route is larger than threekilometers, the vehicle is considered to have detoured from the plannedroute.

In another alternative method, the vehicle controller 108 can detect adetour of the vehicle from a planned route by measuring the tripdistance. Detecting a detour by comparing the vehicle's traveleddistance and the planned trip distance is conducted at the end of thetrip, in some embodiments. The end of the trip time is determined by thedistance of the current vehicle's GPS location and the trip endlocation. If the distance is shorter than a threshold, the vehicle isconsidered to have reached the end of the trip.

When the vehicle reaches the end of the trip, the actual distance drivenis calculated by the vehicle controller 108 from the odometer differenceat the start and the end of the trip. If this driven distance is morethan two kilometers shorter or longer than D_(trip) (which the vehiclecontroller 108 received at the start of the trip when the DTE iscalculated by the service provider 102), the vehicle is considered tohave made a detour from the planned trip route.

In some embodiments, the feedback comparator module 118 can be executedto compare an actual trip energy consumption with the predicted tripenergy consumption. After a trip is completed, the actual energyconsumption of the trip is calculated by the feedback comparator module118 from the battery energy available difference at the start and end ofthe trip such that E_(trip)^(actual)=E_(bat)(t_(start))−E_(bat)(t_(end)). If the vehicle does notmake any detour from the planned trip route, the difference (or relativedifference) between E_(trip) ^(actual) and E_(trip) ^(pred) may becalculated by the feedback comparator module 118. The predicted andactual energy consumption in each of the four categories E_(dr), E_(cl),E_(acc), E_(lo) can also be compared by the feedback comparator module118. By looking at the difference between the predicted value and theactual value in each category, the feedback comparator module 118 cangenerate notifications to the driver (displayed on the HMI of thevehicle 104) to explain what caused the previous DTE estimation error,if the previous DTE estimation was inaccurate.

For example, if the difference in energy for the climate controlcategory is significant, the feedback comparator module 118 could informthe driver that the previous DTE estimation error was due to theunexpected energy usage from the climate control system. If thedifference in the energy for driving the wheels is significant, thefeedback comparator module 118 may first compare the predicted trip timewith the actual trip time. If the trip time difference is significant,the cause of the DTE inaccuracy could be the traffic informationinaccuracy. Otherwise, the feedback comparator module 118 can infer thatthere is a model error. The feedback comparator module 118 can thenexecute a process whereby the model is updated by the efficiencyprediction module 120 of the service provider 102.

As noted, when the vehicle is running periodically (such as every fiveminutes), some summary values that are calculated and temporarily storedmay be sent to the service provider 102. Example calculated valuesinclude energy used for propelling the wheels of the vehicle, energyregenerated from braking, energy for climate control, energy foraccessories, energy loss and energy for low temperature, average speed,speed variance, average propelling torque, propelling torque variance,positive propelling torque time, average brake torque, brake torquevariance, positive brake torque time, distance drive, battery (e.g.,energy source) energy available at the start (and the end) of theperiod, battery state of charge at the start (and the end) of theperiod, and GPS location at the start (and the end) of the period—justto name a few examples.

In some embodiments, some data types are collected and stored upon theoccurrence of a triggering condition or event. The following data arestored when a certain event occurs. Usually the events are “valuechanges” if not specified. The value along with the event time stampwill be temporarily stored onboard and sent to the service provider 102in some embodiments. Example data includes cabin temperature, ambienttemperature, DTE API call input and response (e.g., communicationsbetween the vehicle 104 and the service provider 102 during DTEcalculations), climate settings, regeneration braking setting and drivemodes, a user profile, a trailer status (connected or disconnected), andpower-to-the-box—just to name a few.

FIG. 4 illustrates an example flowchart of a method of the presentdisclosure. The method generally includes a step 402 of determining aplurality of learned parameters of vehicle operation of a vehicle basedon a plurality of energy consumption parameters of the vehicle. Thesedata can be determined from various historical energy consumption ratesfor various systems within the vehicle. Again, these learned parameterscan include learned road class efficiency for the vehicle, learnedtemperature-based energy loss for the vehicle, a learned coldstart-based energy loss for the vehicle, learned transient extra energyfor climate control, learned efficiency for climate control, and learnedefficiency for vehicle accessories. These learned parameters can be usedto calculate energy consumption parameters such as a driving-basedenergy consumption parameter, a temperature-based energy consumptionparameter, a climate control-based energy consumption parameter, and avehicle accessories-based energy consumption parameter.

The method also includes a step 404 of determining a plurality ofpredictive parameters of vehicle operation of the vehicle selected fromany combination of weather data or navigation data, the navigation databeing determined relative to a planned route. This can include obtainingweather data, traffic data, elevation change data for various roadclasses, and so forth.

The method also includes a step 406 of applying a distance-to-empty(DTE) function for the energy source of the vehicle. To be sure, thedistance-to-empty function utilizes the plurality of learned parametersof vehicle operation and the plurality of predictive parameters ofvehicle operation of the vehicle, as well as a current capacity of theenergy source to determine a DTE for the energy source.

EXAMPLE EMBODIMENTS

In some instances, the following examples may be implemented together orseparately by the systems and methods described herein.

Example 1 may include a method, comprising: determining a plurality oflearned parameters of vehicle operation of a vehicle based on aplurality of energy consumption parameters of the vehicle; determining aplurality of predictive parameters of vehicle operation of the vehicleselected from any combination of weather data or navigation data, thenavigation data being determined relative to a planned route; andapplying a distance-to-empty (DTE) function for the energy source, theDTE function utilizing the plurality of learned parameters of vehicleoperation, the plurality of predictive parameters of vehicle operationof the vehicle, and a current capacity of the energy source to determinea DTE for the energy source.

Example 2 may include the method according to example 1, wherein theplurality of predictive parameters of vehicle operation furthercomprises road class data and the weather data includes at leasttemperature data, wherein the road class data comprises real-timetraffic speed, average road slope, and real-time traffic conditions.

Example 3 may include the method according to example 1 and/or someother example herein, wherein the plurality of learned parameters ofvehicle operation of the vehicle are collected over time and withreference to a specific driver, and the plurality of learned parametersof vehicle operation of the vehicle further comprises driving style,climate control preferences, and an average weekly vehicle operationschedule.

Example 4 may include the method according to example 1 and/or someother example herein, wherein the DTE is calculated for any of a tripperiod of the planned route or a post-trip period.

Example 5 may include the method according to example 1 and/or someother example herein, further comprising: determining an actual energyconsumption rate; and updating the DTE function based on deviationsbetween the actual energy consumption rate and the historical energyconsumption rate.

Example 6 may include the method according to example 5 and/or someother example herein, wherein the deviations between the actual energyconsumption rate and the historical energy consumption rate arecalculated and the DTE function is only updated when the vehicleutilizes the planned route.

Example 7 may include the method according to example 1 and/or someother example herein, wherein when the planned route is complete the DTEfunction is used to re-estimate the DTE by calculating a predictedoverall energy consumption rate by: determining a distance of apost-trip operation of the vehicle; estimating an energy consumption ofthe post-trip operation; and estimating an energy consumption rate ofthe post-trip operation based on the distance and the energy consumptionestimated for the post-trip operation.

Example 8 may include a method for estimating energy consumption for avehicle, the method comprising: determining a plurality of energyconsumption parameters for a vehicle on a planned route by: calculatinga first driving-based energy consumption parameter based on anycombination of a distance that the vehicle will travel on a plannedroute over a specified road class, real-time traffic data, and a learnedroad class efficiency for the vehicle; calculating a firsttemperature-based energy consumption parameter based on any combinationof a learned temperature-based energy loss for the vehicle based onoutdoor temperature data, and a learned cold start-based energy loss forthe vehicle which is a function of the outdoor temperature data and acurrent vehicle temperature; calculating a first climate control-basedenergy consumption parameter based on any combination of a learnedtransient extra energy for climate control, which is a function of aninitial cabin temperature data and the outdoor temperature data, and alearned efficiency for climate control which is a function of theoutdoor temperature data; and calculating a first vehicleaccessories-based energy consumption parameter based on a learnedefficiency for vehicle accessories; and estimating the energyconsumption of the vehicle using the plurality of energy consumptionparameters.

Example 9 may include the method according to example 8, furthercomprising calculating an energy consumption for operating the vehicleafter completion of the planned route by: calculating a seconddriving-based energy consumption parameter based on an expected energyconsumption for driving the vehicle; calculating a secondtemperature-based energy consumption parameter based on any combinationof the learned temperature-based energy loss for the vehicle based onfuture outdoor temperature data, and a learned cold start-based energyloss for the vehicle which is a function of the future outdoortemperature data and a predicted vehicle temperature; calculating asecond climate control-based energy consumption parameter based on anycombination of a learned transient extra energy for climate control,which is a function of a predicted initial cabin temperature data andfuture outdoor temperature data, and a learned efficiency for climatecontrol which is a function of the future outdoor temperature data; andutilizing the first vehicle accessories-based energy consumptionparameter.

Example 10 may include the method according to example 8 and/or someother example herein, wherein the learned road class efficiency for thevehicle comprises energy rates for driving the vehicle on a plurality ofroad classes.

Example 11 may include the method according to example 8 and/or someother example herein, wherein the learned efficiency for climate controlcomprises steady state energy rates for climate control of the vehiclecalculated over a range of outdoor temperature values.

Example 12 may include the method according to example 8 and/or someother example herein, wherein the learned transient extra energy forclimate control comprises a calculation of extra energy usage forclimate control of the vehicle to ensure that a cabin temperature of thecabin is set to a target temperature.

Example 13 may include the method according to example 8 and/or someother example herein, wherein the learned temperature-based energy lossand the learned cold start-based energy loss each comprise steady stateenergy rate losses calculated over a range of outdoor temperaturevalues.

Example 14 may include the method according to example 8 and/or someother example herein, further comprising determining operating patternparameters of the vehicle that comprise any of: an average drivingdistance in each of a plurality of time windows; an average driving timein each of the plurality of time windows; an average number of trip coldstarts in each of the plurality of time windows; an average of the firstdriving-based energy consumption parameter calculated over each of theplurality of time windows; and an average temperature difference at tripcold starts in each of the plurality of time windows.

Example 15 may include the method according to example 8 and/or someother example herein, further comprising displaying the energyconsumption of the vehicle on a human machine interface of the vehicle.

Example 16 may include a system, comprising: a vehicle controllercomprising at least one processor and a memory, the at least oneprocessor executing instructions stored in the memory to: determine aplurality of learned parameters of vehicle operation of a vehicle basedon at least a historical energy consumption rate for an energy source ofthe vehicle; determine a plurality of predictive parameters of vehicleoperation of the vehicle selected from any combination of weather dataor navigation data, the navigation data being determined relative to aplanned route; and apply a distance-to-empty (DTE) function for theenergy source, the DTE function utilizing the plurality of learnedparameters of vehicle operation, and the plurality of predictiveparameters of vehicle operation of the vehicle, based on a currentcapacity of the energy source to determine a DTE for the vehicle.

Example 17 may include the system according to example 16, wherein theplurality of predictive parameters of vehicle operation furthercomprises road class data and the weather data includes at leasttemperature data, wherein the road class data comprises real-timetraffic speed, average road slope, and real-time traffic conditions, andfurther wherein the plurality of learned parameters of vehicle operationof the vehicle are collected over time and with reference to a specificdriver, the plurality of learned parameters of vehicle operation of thevehicle further comprise driving style, climate control preferences, andan average weekly vehicle operation schedule.

Example 18 may include the system according to example 16 and/or someother example herein, wherein the at least one processor furtherexecutes the instructions stored in the memory to: sense an occurrenceof a triggering condition; re-determine the plurality of learnedparameters of vehicle operation of the vehicle based on the historicalenergy consumption rate for an energy source of the vehicle in view ofthe triggering condition; re-determine the plurality of predictiveparameters of vehicle operation of the vehicle selected from anycombination of the weather data or the navigation data in view of thetriggering condition; and apply the distance-to-empty (DTE) functionthat utilizes the re-determined plurality of learned parameters ofvehicle operation, and the re-determined plurality of predictiveparameters of vehicle operation of the vehicle to re-calculate the DTEfor the vehicle in view of the triggering condition.

Example 19 may include the system according to example 18 and/or someother example herein, wherein the triggering condition comprisesconnection of a trailer to the vehicle.

Example 20 may include the system according to example 16 and/or someother example herein, wherein the at least one processor furtherexecutes the instructions stored in the memory to: determine an actualenergy consumption rate; and update the DTE function based on deviationsbetween the actual energy consumption rate and the historical energyconsumption rate, wherein when the planned route is complete and thecurrent capacity of the energy source is at or above an empty thresholdthe at least one processor further executes the instructions stored inthe memory to: utilize the DTE function to re-estimate the DTE bycalculating a predicted overall energy consumption rate by: determininga distance of a post-trip operation of the vehicle; estimating an energyconsumption of the post-trip operation; and estimating an energyconsumption rate of the post-trip operation based on the distance andthe energy consumption estimated for the post-trip operation.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, which illustrate specificimplementations in which the present disclosure may be practiced. It isunderstood that other implementations may be utilized, and structuralchanges may be made without departing from the scope of the presentdisclosure. References in the specification to “one embodiment,” “anembodiment,” “an example embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, one skilled in the art will recognizesuch feature, structure, or characteristic in connection with otherembodiments whether or not explicitly described.

Implementations of the systems, apparatuses, devices, and methodsdisclosed herein may comprise or utilize a special purpose orgeneral-purpose computer including computer hardware, such as, forexample, one or more processors and system memory, as discussed herein.Implementations within the scope of the present disclosure may alsoinclude physical and other computer-readable media for carrying orstoring computer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that stores computer-executable instructions iscomputer storage media (devices). Computer-readable media that carriescomputer-executable instructions is transmission media. Thus, by way ofexample, and not limitation, implementations of the present disclosurecan comprise at least two distinctly different kinds ofcomputer-readable media: computer storage media (devices) andtransmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (SSDs) (e.g., based on RAM), flash memory,phase-change memory (PCM), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or anycombination of hardwired or wireless) to a computer, the computerproperly views the connection as a transmission medium. Transmissionmedia can include a network and/or data links, which can be used tocarry desired program code means in the form of computer-executableinstructions or data structures and which can be accessed by a generalpurpose or special purpose computer. Combinations of the above shouldalso be included within the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. Thecomputer-executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the present disclosure maybe practiced in network computing environments with many types ofcomputer system configurations, including in-dash vehicle computers,personal computers, desktop computers, laptop computers, messageprocessors, handheld devices, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,pagers, routers, switches, various storage devices, and the like. Thedisclosure may also be practiced in distributed system environmentswhere local and remote computer systems, which are linked (either byhardwired data links, wireless data links, or by any combination ofhardwired and wireless data links) through a network, both performtasks. In a distributed system environment, program modules may belocated in both the local and remote memory storage devices.

Further, where appropriate, the functions described herein can beperformed in one or more of hardware, software, firmware, digitalcomponents, or analog components. For example, one or more applicationspecific integrated circuits (ASICs) can be programmed to carry out oneor more of the systems and procedures described herein. Certain termsare used throughout the description and claims refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein for purposes of illustration and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the present disclosure have been directedto computer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer-usable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentdisclosure. Thus, the breadth and scope of the present disclosure shouldnot be limited by any of the above-described exemplary embodiments butshould be defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the presentdisclosure. For example, any of the functionality described with respectto a particular device or component may be performed by another deviceor component. Further, while specific device characteristics have beendescribed, embodiments of the disclosure may relate to numerous otherdevice characteristics. Further, although embodiments have beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the disclosure is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as illustrative forms ofimplementing the embodiments. Conditional language, such as, amongothers, “can,” “could,” “might,” or “may,” unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments could include,while other embodiments may not include, certain features, elements,and/or steps. Thus, such conditional language is not generally intendedto imply that features, elements, and/or steps are in any way requiredfor one or more embodiments.

That which is claimed is:
 1. A method for estimating energy consumptionfor a vehicle, the method comprising: determining a plurality of energyconsumption parameters for a vehicle on a planned route by: calculatinga first driving-based energy consumption parameter based on anycombination of a distance that the vehicle will travel on a plannedroute over a specified road class, real-time traffic data, and a learnedroad-class efficiency for the vehicle; calculating a firsttemperature-based energy consumption parameter based on any combinationof a learned temperature-based energy loss for the vehicle based onoutdoor temperature data, and a learned cold start-based energy loss forthe vehicle which is a function of the outdoor temperature data and acurrent vehicle temperature; and calculating a first climatecontrol-based energy consumption parameter based on any combination of alearned transient extra energy for climate control, which is a functionof an initial cabin temperature data and the outdoor temperature data,and a learned efficiency for climate control which is a function of theoutdoor temperature data; determining, via a machine learning module, aplurality of operating parameters of the vehicle based on the pluralityof energy consumption parameters, wherein the plurality of operatingparameters comprises an average vehicle weekly operation schedule havingan average driving distance in each time window, an average driving timein the each time window, an average number of trip cold starts in theeach time window, an average energy for driving in the each time window,and an average temperature difference between inside the vehicle andoutside the vehicle at trip cold starts in the each time window; andestimating the energy consumption of the vehicle using the plurality ofenergy consumption parameters.
 2. The method according to claim 1,further comprising calculating an energy consumption for operating thevehicle after completion of the planned route by: calculating a seconddriving-based energy consumption parameter based on an expected energyconsumption for driving the vehicle; calculating a secondtemperature-based energy consumption parameter based on any combinationof the learned temperature-based energy loss for the vehicle based onfuture outdoor temperature data, and a learned cold start-based energyloss for the vehicle which is a function of the future outdoortemperature data and a predicted vehicle temperature; calculating asecond climate control-based energy consumption parameter based on anycombination of a learned transient extra energy for climate control,which is a function of a predicted initial cabin temperature data andfuture outdoor temperature data, and a learned efficiency for climatecontrol which is a function of the future outdoor temperature data; andutilizing the first vehicle accessories-based energy consumptionparameter.
 3. The method according to claim 1, wherein the learned roadclass efficiency for the vehicle comprises energy rates for driving thevehicle on a plurality of road classes.
 4. The method according to claim1, wherein the learned efficiency for climate control comprises steadystate energy rates for climate control of the vehicle calculated over arange of outdoor temperature values.
 5. The method according to claim 1,wherein the learned transient extra energy for climate control comprisesa calculation of extra energy usage for climate control of the vehicleto ensure that a cabin temperature of the cabin is set to a targettemperature.
 6. The method according to claim 1, wherein the learnedtemperature-based energy loss and the learned cold start-based energyloss each comprise steady state energy rate losses calculated over arange of outdoor temperature values.
 7. The method according to claim 1,further comprising displaying the energy consumption of the vehicle on ahuman machine interface of the vehicle.
 8. The method according to claim1, further comprising: applying a distance-to-empty (DTE) function forthe energy source, the DTE function utilizing at least the plurality ofoperating parameters and a current capacity of the energy source todetermine a DTE for the energy source.